• Design, develop, and deploy AI/ML solutions leveraging **LLMs, NLP, and Generative AI** • Build and optimize **Retrieval-Augmented Generation (RAG)** pipelines using vector databases and embedding models • Develop **agentic AI systems** (multi-step reasoning, tool use, orchestration frameworks) • Fine-tune, prompt-engineer, and evaluate large language models for production use cases • Build scalable, end-to-end ML/AI pipelines including data ingestion, preprocessing, model training, and deployment • Integrate AI solutions into applications via APIs and microservices • Collaborate with cross-functional teams (data engineers, product managers, and business stakeholders) to define AI-driven solutions • Implement **model monitoring, evaluation frameworks, and guardrails** (bias, hallucination mitigation, safety) • Optimize models and pipelines for performance, scalability, and cost-efficiency in cloud environments • Translate complex AI outputs into actionable insights for both technical and non-technical audiences • Contribute to AI best practices, architecture decisions, and internal tooling • Mentor junior engineers and guide teams on modern AI development patterns

Position SummaryThe Machine Learning Engineer will be responsible for the end-to-end development and deployment of Large language and machine learning models, with a primary focus on data preprocessing, model training, and fine-tuning using large-scale healthcare datasets. This role requires a strong understanding of Large language models, machine learning principles, data engineering, and experience working with sensitive healthcare data. Key Responsibilities - Data Preprocessing: Clean, transform, and prepare large, complex healthcare datasets for machine learning model development. This includes handling missing values, outlier detection, feature engineering, and data normalization. Identify, collect, and curate relevant, industry-specific datasets for model retraining. Format data appropriately for the chosen LLM and training pipeline - Model Training & Fine-Tuning: Design, train, and fine-tune various LLMs on extensive healthcare data to solve specific clinical or operational problems. Set up and manage the training environment, including GPU instances and required software. Train and fine-tune pre-trained LLMs on the custom dataset to achieve specific goals. Experiment with and fine-tune hyperparameters such as learning rate, batch size, and training epochs to optimize model performance. Integration of structured + unstructured data (multi-modal/multi-input models) - Model Evaluation & Optimization: Evaluate model performance using appropriate metrics, identify areas for improvement, and implement optimization strategies. - Pipeline Development: Develop and maintain robust and scalable data and ML pipelines for model training, inference, and deployment. - Collaboration: Work closely with data scientists, clinicians, and software engineers to understand requirements, integrate models into production systems, and ensure data privacy and security compliance. - Research & Development: Stay up-to-date with the latest advancements in machine learning and healthcare AI, and explore new technologies and methodologies to enhance our solutions. - Documentation: Maintain clear and comprehensive documentation of models, data pipelines, and experimental results.

• Build and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent • Design and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models • Develop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers • Optimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance • Design observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health • Manage model registries and CI/CD pipelines enabling automated and reproducible model deployments • Own the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities • Define engineering best practices and contribute to platform scalability in a fast-moving startup environment

Role Description At Leap Tools, we are building the world's most advanced solutions for the interior décor industry. Our technology lets you preview products in your own room before you buy them. You can redesign your home and ensure that new tiles or rugs will look good in your space through our proprietary cutting-edge technology. What You'll Do - Innovative Vision: Spearhead machine learning initiatives to instantly generate and visualize a vast array of home décor items, transforming browsers into personalized interior design studios. - Holistic Redecoration: Use state-of-the-art algorithms to offer users an all-encompassing redecoration experience, seamlessly integrating wallpaper, flooring, countertops, and a plethora of other home essentials. - Adaptive Product Rendering: Implement machine learning techniques to adapt and present décor products in real-time based on user preferences, room dimensions, and existing furnishings. - User-Centric Design: Work closely with interior design experts, UX/UI designers, and development teams to constantly refine models and enhance realism. Qualifications - Your expertise in Machine Learning isn’t just about models and algorithms; it’s about driving tangible, user-centric solutions. - You are willing and able to tackle unsolved problems without supervision or much guidance after you collaborate with your team members on initial designs. - You are persistent, despite days, weeks, or even months of disappointing results, maintaining the same level of determination as on day one. - You have a strong foundation in computer science and are very comfortable coding in Python. - You have read papers on machine learning topics and implemented your own versions of the papers. - You're hands-on with data: from collection and cleaning to putting together evaluation sets. - Challenges? Bring them on. You thrive when you're pushing the boundaries of what Machine Learning can achieve. Requirements - Mostly PyTorch, some Tensorflow, as well as our own in-house systems. - Python and Django. - gRPC. - Kubernetes on AWS. Benefits - Remote-first company that encourages employees to work from where they're most productive. - Tight-knit teams to cultivate an ownership mentality. - Encouragement of curiosity and attention to detail. - Work anywhere in the world for up to 3 months! - Parental leave program. - Work-from-home stipend. - Your birthday (and our company's birthday) is a day off! Company Description Leap Tools is an equal opportunity employer committed to fostering an inclusive, equitable, and accessible environment. Accommodations are available on request for candidates taking part in all aspects of the interview process.